File size: 20,009 Bytes
e0c1c90
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
---
base_model: colorfulscoop/sbert-base-ja
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:680
- loss:CoSENTLoss
widget:
- source_sentence: 中を見てみよう
  sentences:
  - 外を調べよう
  - リリアンはどんな魔法が使えるの?
  - 花がぬいぐるみに変えられている
- source_sentence: キャンドル要らない
  sentences:
  - なんで猫が話せる?
  - 自分でやれば?
  - 中を見てみよう
- source_sentence: 信用できない
  sentences:
  - どっちでもいいよ
  - 誰?
  - 誰かが呪文で花をぬいぐるみに変えた
- source_sentence: 例えば?
  sentences:
  - 誰かがが魔法をかけた
  - ジャック
  - なんでしなきゃいけないの?
- source_sentence: 魔法を使える人
  sentences:
  - かっこいいね
  - 物の姿を変えられる人
  - 町って?
model-index:
- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: custom arc semantics data jp
      type: custom-arc-semantics-data-jp
    metrics:
    - type: cosine_accuracy
      value: 0.9044117647058824
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.5501536726951599
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.912751677852349
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.4790937304496765
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.918918918918919
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9066666666666666
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9084179566135925
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.9117647058823529
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 294.13421630859375
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.9166666666666666
      name: Dot F1
    - type: dot_f1_threshold
      value: 294.13421630859375
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.9565217391304348
      name: Dot Precision
    - type: dot_recall
      value: 0.88
      name: Dot Recall
    - type: dot_ap
      value: 0.915716305189008
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.9044117647058824
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 482.6566162109375
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.913907284768212
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 532.9744262695312
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.9078947368421053
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.92
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9104676924615509
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.9117647058823529
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 23.818954467773438
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.918918918918919
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 23.818954467773438
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.9315068493150684
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9066666666666666
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9093211275077335
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.9117647058823529
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 482.6566162109375
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.918918918918919
      name: Max F1
    - type: max_f1_threshold
      value: 532.9744262695312
      name: Max F1 Threshold
    - type: max_precision
      value: 0.9565217391304348
      name: Max Precision
    - type: max_recall
      value: 0.92
      name: Max Recall
    - type: max_ap
      value: 0.915716305189008
      name: Max Ap
---

# SentenceTransformer based on colorfulscoop/sbert-base-ja

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '魔法を使える人',
    '物の姿を変えられる人',
    'かっこいいね',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Binary Classification
* Dataset: `custom-arc-semantics-data-jp`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.9044     |
| cosine_accuracy_threshold    | 0.5502     |
| cosine_f1                    | 0.9128     |
| cosine_f1_threshold          | 0.4791     |
| cosine_precision             | 0.9189     |
| cosine_recall                | 0.9067     |
| cosine_ap                    | 0.9084     |
| dot_accuracy                 | 0.9118     |
| dot_accuracy_threshold       | 294.1342   |
| dot_f1                       | 0.9167     |
| dot_f1_threshold             | 294.1342   |
| dot_precision                | 0.9565     |
| dot_recall                   | 0.88       |
| dot_ap                       | 0.9157     |
| manhattan_accuracy           | 0.9044     |
| manhattan_accuracy_threshold | 482.6566   |
| manhattan_f1                 | 0.9139     |
| manhattan_f1_threshold       | 532.9744   |
| manhattan_precision          | 0.9079     |
| manhattan_recall             | 0.92       |
| manhattan_ap                 | 0.9105     |
| euclidean_accuracy           | 0.9118     |
| euclidean_accuracy_threshold | 23.819     |
| euclidean_f1                 | 0.9189     |
| euclidean_f1_threshold       | 23.819     |
| euclidean_precision          | 0.9315     |
| euclidean_recall             | 0.9067     |
| euclidean_ap                 | 0.9093     |
| max_accuracy                 | 0.9118     |
| max_accuracy_threshold       | 482.6566   |
| max_f1                       | 0.9189     |
| max_f1_threshold             | 532.9744   |
| max_precision                | 0.9565     |
| max_recall                   | 0.92       |
| **max_ap**                   | **0.9157** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### csv

* Dataset: csv
* Size: 680 training samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 680 samples:
  |         | text1                                                                            | text2                                                                            | label                                           |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                           | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
* Samples:
  | text1                       | text2                       | label          |
  |:----------------------------|:----------------------------|:---------------|
  | <code>いらない</code>           | <code>うんよろしく</code>         | <code>0</code> |
  | <code>足元よりも更に深くってどこ?</code> | <code>足元よりも更に深くってなに?</code> | <code>1</code> |
  | <code>他にはないの?</code>        | <code>どう思う?</code>          | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### csv

* Dataset: csv
* Size: 680 evaluation samples
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
* Approximate statistics based on the first 680 samples:
  |         | text1                                                                            | text2                                                                            | label                                           |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                           | int                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
* Samples:
  | text1                    | text2                    | label          |
  |:-------------------------|:-------------------------|:---------------|
  | <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code>    | <code>1</code> |
  | <code>夕飯は何だったの?</code>   | <code>チキンヌードル食べた?</code> | <code>0</code> |
  | <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code>      | <code>0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `num_train_epochs`: 13
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 13
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   | custom-arc-semantics-data-jp_max_ap |
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
| None  | 0    | -             | -      | 0.8596                              |
| 1.0   | 68   | 2.6802        | 1.7807 | 0.8872                              |
| 2.0   | 136  | 1.4014        | 1.7683 | 0.8945                              |
| 3.0   | 204  | 0.7937        | 1.9877 | 0.9039                              |
| 4.0   | 272  | 0.5443        | 1.9106 | 0.9075                              |
| 5.0   | 340  | 0.4225        | 1.9418 | 0.9109                              |
| 6.0   | 408  | 0.3347        | 2.0123 | 0.9107                              |
| 7.0   | 476  | 0.3425        | 2.0387 | 0.9094                              |
| 8.0   | 544  | 0.2427        | 1.9878 | 0.9103                              |
| 9.0   | 612  | 0.2412        | 2.0424 | 0.9178                              |
| 10.0  | 680  | 0.1623        | 2.0273 | 0.9188                              |
| 11.0  | 748  | 0.1909        | 2.0955 | 0.9220                              |
| 12.0  | 816  | 0.1507        | 2.2124 | 0.9157                              |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->